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• W4232678866 abstract "We appreciate Dr Andrei’s comments [1Andrei A.-C. Modeling hospital length of stay data: pitfalls and opportunities (letter).Ann Thorac Surg. 2016; 101: 2426PubMed Scopus (4) Google Scholar] on our study [2Ad N. Holmes S.D. Shuman D.J. et al.Potential impact of modifiable clinical variables on length of stay after first-time cardiac surgery.Ann Thorac Surg. 2015; 100: 2102-2108Abstract Full Text Full Text PDF PubMed Scopus (8) Google Scholar] in which we sought to assess the reliability of the Society of Thoracic Surgeons (STS) risk prediction for extended and shorter LOS and examine whether modifiable clinical variables are associated with length of stay (LOS) in first-time cardiac surgery patients. We examined the data from more than 3,000 cardiac surgery patients at our center and found that the STS risk model was reliably predictive of short and extended LOS. In addition, our analyses found that potentially modifiable clinical variables, such as low preoperative hematocrit and blood transfusion, were predictive of longer LOS in this sample of patients. We agree with Dr Andrei about the importance of avoiding overfitting and having well-calibrated predictive models. These concepts are many times overlooked by researchers, which can result in unreliable and nonreplicable findings from multivariate regression analyses. However, it appears that Dr Andrei has misinterpreted some of the results presented in our manuscript. Namely, Dr Andrei suggests that we used our own single center regression model to calculate the observed versus expected (O/E) ratios for LOS, when in actuality and as clearly stated in our paper, we used the STS-predicted values for the O/E ratios. The STS risk models, developed using multivariate logistic regression, are well-calibrated and validated and recognized as appropriate to use in an O/E ratio context. We are therefore uncertain as to why Dr Andrei questions the validity of our O/E analysis findings, which are simply a comparison of the observed values from our center and the expected values from the STS risk prediction models. In fact, these O/E analyses were conducted simply to assess the reliability of a previously established scoring system, the STS risk prediction model, in our own center. These results were carefully separated into a separate Results section from the findings we present regarding the second aim of our study. To clarify further, the second aim of our study was to examine whether modifiable clinical variables are associated with LOS in patients undergoing first-time aortic valve, mitral valve, or coronary artery bypass graft surgery at our institution, not to develop a risk scoring system. The multivariate linear model predicting LOS as a continuous variable was constructed from clinically and theoretically relevant covariates and potentially modifiable factors. After determining whether any modifiable factors were independent predictors of LOS, we used the regression equation from the full model to examine hypothetical patient characteristics in order to show the clinical relevance of the modifiable factors in the context of this predictive model. Moreover, the scatterplot in Figure 2 and associated correlation between actual and predicted LOS from this model were shown to examine the accuracy of this model. In the discussion of our findings regarding the linear regression model that we constructed, we do not suggest that other clinicians or centers use our regression analysis as a prediction model. Rather, we use the results from our single center regression analyses to propose that modifiable variables associated with LOS should be considered, especially in elective surgery, so that their impact on LOS can be ameliorated. Developing protocols and practices to account for these factors should lead to shorter LOS, more satisfactory outcomes, and reduced financial burden after cardiac surgery. Modeling Hospital Length of Stay Data: Pitfalls and OpportunitiesThe Annals of Thoracic SurgeryVol. 101Issue 6PreviewHospital length of stay (LOS) represents an important resource utilization aspect in adult cardiac surgery. Ad and colleagues’ [1] recent paper identifies significant modifiable clinical variables that are predictive of LOS. A multivariate linear regression predictive model is used to produce expected LOS values and observed-to-expected summary ratios are created for short (<6 days) and extended LOS (>14 days). This ratio is equal to 1.4 in the first group and 0.84 in the second group. Full-Text PDF" @default.
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• W4232678866 date "2016-06-01" @default.
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• W4232678866 doi "https://doi.org/10.1016/j.athoracsur.2016.02.052" @default.
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